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HUMAINT - Face Recognition

19th October 2022 at 6:15pm

Co-Mining: Deep Face Recognition with Noisy Labels

19th October 2022 at 7:24pm

We still have a limited understanding of how to train the CNN models with the label noise inherent in existing face recognition datasets. To address this issue, this paper develops a novel co-mining strategy to effectively train on the datasets with noisy labels. Specifically, we simultaneously use the loss values as the cue to detect noisy labels, exchange the high confidence clean faces to alleviate the errors accumulated issue caused by the sample-selection bias, and re-weight the predicted clean faces to make them dominate the discriminative model training in a mini-batch fashion.

Methodology

Noisy Labels Detection

Intuitively, when labels are correct, small-loss instances are more likely to be the ones which are correctly labeled. Therefore, if we train our classifier only using small-loss instances in each minibatch data, it should be resistant to noisy labels. Assume that we have estimated the noise rate rr of a face recognition dataset. MM is the mini-batch size. Our method maintains two networks simultaneously. In each mini-batch of data, each peer network views its small-loss instances as the useful knowledge and drops about [rโˆ—M][r โˆ— M] numbers of big-loss instances as the distractors, leaving the rest of samples into two parts, intersected faces and non-intersected faces of these two peer networks. For the intersected faces, since two peer networks predict them as clean faces, we have reason to believe that they are clean enough for deep face recognition. For the non intersected ones, they have high confidence to be clean faces.

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